The disclosure relates generally to systems and methods for treating schizophrenia and, more particularly, to systems and methods for modeling and predicting effective treatments, including drug treatments for schizophrenia.
The global burden of major neuropsychiatric illnesses, such as schizophrenia, bipolar illness, major depression, and Alzheimer's disease, is immense (World Health Organization 2001), owing both to the chronic and highly debilitating nature of these conditions and to their relatively high prevalence (Kessler et al. 2005). Current pharmacologic treatments for these diseases vary in efficacy, with most being incompletely effective, and many carrying significant side effect burdens (J. T. Coyle et al. 2010). Despite considerable amounts of psychopharmacologic research in recent decades, no agents with fundamentally new mechanisms of action have been identified, and by most accounts, there is little to speak of in the developmental pipeline (Nestler and Hyman 2010). This is particularly glaring in light of the explosion of neuroscience research over the past ten to twenty years—this work has led to a vast body of knowledge on the neurobiological correlates of many of these conditions, but this had sadly not led to the breakthrough medications that had been anticipated.
One possible reason psychiatry has lagged other medical specialties in arriving at more effective treatments is that the field has focused on clinical phenomenology, rather than etiology, to define illnesses—an important constraint, given the lack of understanding of underlying cause of most psychiatric diseases. Indeed, currently psychiatric illnesses are defined by particular constellations of signs and symptoms, as detailed in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) (American Psychiatric Association 2013). Thus, a drug trial for the treatment of depression, for example, may include a highly heterogeneous group of patients from the point of view of underlying biological processes (Singh and Rose 2009). Over the past ten or so years, however, there has been a sea change in the manner in which researchers are attempting to understand psychiatric illnesses, with a new emphasis on the use of rigorously defined biological markers, or biomarkers, rather than clinical impression alone (Miller 2010). A biomarker is defined as “a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention.” (Biomarkers Definitions Working Group 2001). Thus, biomarkers lie at an intermediate level, between biological and genetic underpinnings and clinically observable symptoms—as such, focusing on these readouts may be a more fruitful way forward, both in terms of categorizing psychiatric illness (Casey et al. 2014) and arriving at more effective and targeted treatments (Meyer-Lindenberg and Weinberger 2006). In recent years, a number of biomarkers for several central nervous system (CNS) disorders have been proposed (Perlis 2011).
The clear technical difficulties in accessing and manipulating the human brain at the neural level have made it necessary, practically speaking, to instantiate biomarkers of psychiatric illness in an in vivo or in vitro manner as pharmacologic test systems. Often these involve developing biomarker-based assays in rodents and assessing potential medications' ability to affect these measures. However, this approach can present problems, for a number of reasons. First, the dysfunction characteristic of the disorders in question—e.g., the paranoid delusions seen in schizophrenia, the extreme dysphoria associated with depression—may, in many ways, be uniquely human. Part and parcel of this disconnect is that rodent, and even non-human primate brains are not strictly analogous with human brains, and these dissimilarities could be very significant in terms of these illnesses. The prefrontal cortex (PFC), for example shows marked increases in size and complexity as one ascends the evolutionary ladder (Ongur and Price 2000; Defelipe 2011). Also, while animal models are certainly more manipulable and query-able in comparison with humans, in these model systems, the rodent brain still remains a “black box,” whose workings, at a mechanistic, causal level, are by no means transparent.
With the historical emphasis on in vitro and in vivo animal models of psychiatric illness, the potential for computational, or in silico, models to help develop pharmacologic treatments for these conditions has remained largely untapped. One research group has outlined a process for using computational approaches to screen candidate psychiatric medications—that is, to evaluate the potential efficacy and side effect burden of existing candidate agents, based on their affinity at known neurotransmitter receptors (Geerts et al. 2012). This could potentially play a role at one point in the drug development process, when a putative agent has been characterized molecularly, and its effects on known receptors have been analyzed and quantified. However, such a construct limits the ability to develop truly novel agents.
Therefore, a need persists for systems and methods that enable researchers and clinicians to model and predict effective treatments for schizophrenia and other disorders.